PB2274: A PROGNOSTIC MODEL TO PREDICT MORTALITY IN ICU DEPARTMENT OF HEMATOLOGY CLINIC
Authors: A. Luchinin, A. Lyanguzov
Journal: HemaSphere (2022)

Background: The ICU department’s mortality rate is one of the most important indicators of quality of care. The prognosis of mortality is a part of clinical, social, and economic workflows in any clinic, but its measure is a very difficult task that depends on several factors. Aims: Based on real clinical data from the years 2018-2021 to create a prognostic model of mortality for patients with blood diseases in the ICU. Methods: The study included 202 patients in total. The median age was 57 (19-82) years. There were 112 males and 90 females with the following distribution by diagnosis: Hodgkin lymphoma - 12 patients, non-Hodgkin lymphoma and CLL - 40, multiple myeloma - 24, MDS - 12, acute lymphoblastic leukemia - 18, acute myeloid leukemia - 82, chronic myeloproliferative diseases - 6 and other - 2. In all cases, patients were placed in the ICU due to severe conditions infection during disease progression or chemotherapy. In order to calculate the odds ratio (OR), we used a binomial logistic regression approach. A total of 4444 variables were processed for a matrix representation of both the training and the test dataset (80/20 split). The training data included 21 predictors: sex, age, fever, respiratory rate, systolic blood pressure, diastolic blood pressure, mean blood pressure, heart rate, need to inotropic support (Yes/No), blood hypoxemia, HGB level, PLT level, WBC level, creatinine level, CRP level, serum total protein level, serum albumin level, blood total bilirubin, blood procalcitonin, bloodstream infection detection (Yes/No) and Glasgow scale (<>15 scores). The dependent variable was death during the hospitalization in the ICU. All statistical analyses were undertaken using R version 3.4.2 and the “missForest”, “glmnet”, “caret”, “pROC” packages. Results: The absolute risk of death (mortality rate) was 67 from 202 (33%), odds – 0.496. We performed all steps according to assumptions for logistic regression including missing data imputation, multicollinearity test, regularization for feature selection and log numeric data with outliers (log base 2). Our model for “high-risk of death” detection classified patients in the test dataset was with AUC 0.816 accuracy (95%CI 0.679-0.912, McNemar’s Test p-value = 0.5), with sensitivity 0.80 and specificity 0.82. We received a pretty accurate and balanced model with 3 predictors: Glasgow scale (<>15), blood PLT level, and serum total protein level. The equation of the model is Y(probability of death) = 14.13 - 0.28*log2(PLT) -2.37*log2(total protein) + 2.98*1 (if Glasgow score <15 scores) / 0 (if Glasgow score = 15). The odds of death in ICU grow up to ~2 times if the patient has a Glasgow score of less than 15. Also, the odds of death increase by 1.3 and 10.7 times of PLT, or serum total protein level decreases by 2 times accordingly. Summary/Conclusion: Our research revealed the most important predictors of mortality in patients with blood diseases. They are Glasgow score (represents the level of consciousness), PLT level on CBC (represents the bone marrow failure due to blood disease or chemotherapy), and serum total protein level (represents liver function and overall metabolism in the body). The information received can be used in clinical practice and in developing clinical decision support systems.